1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
30/1/2024 Electricidad 55759 Andrés NA
3/2/2024 donación 50000 Andrés NA
4/2/2024 Comida 46309 Andrés NA
5/2/2024 Comida 33079 Tami Supermercado
8/2/2024 Enceres 35440 Andrés casaideas
18/2/2024 VTR 21990 Andrés NA
22/2/2024 Netflix 8393 Tami NA
25/2/2024 Enceres 4973 Andrés colgador manguera
25/2/2024 Enceres 7980 Andrés adaptador vorriente y adaptadores manguera
27/2/2024 Enceres 49980 Andrés detergente
28/2/2024 Enceres 12000 Andrés 2 cajas orgamizadoras
28/2/2024 Electricidad 56337 Andrés PAC ENEL_______ 00000001686518 28/02
29/2/2024 Gas 70997 Andrés 68 997 + propina 2 lks
3/3/2024 Comida 53553 Andrés Supermercado
4/3/2024 Comida 6000 Andrés pasas
5/3/2024 Uber cumple papá 8582 Tami NA
6/3/2024 Agua 16549 Andrés NA
7/3/2024 Enceres 4645 Andrés descuentos desodorantes
10/3/2024 Comida 7470 Andrés NA
10/3/2024 Comida 90504 Tami Supermercado
10/3/2024 Diosi 21081 Andrés pipeta
11/3/2024 Diosi 66970 Andrés n&d 50990 + lavanda 3asy clean 10k 79990x2
17/3/2024 Comida 55951 Tami Supermercado
19/3/2024 VTR 21990 Andrés NA
24/3/2024 Comida 94384 Tami Supermercado
27/3/2024 Comida 27980 Tami Barras Wild Soul
27/3/2024 Electricidad 56338 Andrés NA
29/3/2024 Comida 69144 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.4857e+08   2    8.0756  3e-04 ***
## lag_depvar    1.0058e+11   1 1914.4053 <2e-16 ***
## Residuals     3.5989e+10 685                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   931.1349 13526.54 0.0196518
## 2-0 29308.324 23601.3872 35015.26 0.0000000
## 2-1 22079.486 18730.8790 25428.09 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
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## 232  74875.14             2   80355.00
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## 235  56946.43             2   66062.43
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## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
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## 294  46520.00             2   34704.86
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## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
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## 310  37898.00             2   46423.71
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## 320  73744.86             2   64589.14
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## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
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## 329  25957.14             2   60358.29
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## 331  30681.57             2   30178.43
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## 333  32582.71             2   33337.29
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## 348  22759.57             2   33557.00
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## 354  34030.00             2   29732.29
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## 357  38509.29             2   37066.57
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## 359  49423.00             2   40957.29
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## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
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## 397  58280.43             2   60012.57
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## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
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## 431  39328.29             2   39456.29
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## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
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## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
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## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
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## 471  46170.00             2   47788.57
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## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
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## 492  65416.43             2   56677.43
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## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
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## 497  41368.57             2   40998.57
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## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
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## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
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## 513  39456.86             2   38191.14
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## 518  65569.43             2   56227.14
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## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
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## 527  53924.00             2   48911.00
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## 529  42121.14             2   53358.86
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## 531  62329.29             2   47835.71
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## 534  64511.57             2   59946.43
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## 536  55448.71             2   61137.43
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## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
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## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 685  39336.29             2   39911.57
## 686  39678.86             2   39336.29
## 687  41963.14             2   39678.86
## 688  54220.57             2   41963.14
## 689  63901.86             2   54220.57
## 690  73116.00             2   63901.86
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   533 51542.59 15336.443
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1939.10790   4006.18619   -504.10272   2467.51978  -2902.45973    543.16121 
##            8            9           10           11           12           13 
##  -5619.95080  -1228.22787  -4010.76594   -505.09519  -5014.39970  -1736.39000 
##           14           15           16           17           18           19 
##  -1025.89800    261.98936  -3331.18797   -493.06289  -2228.68723   6495.55011 
##           20           21           22           23           24           25 
##  -1524.66281  -1218.43247   1457.14364  -1174.86706    235.60158   1706.40459 
##           26           27           28           29           30           31 
##  -7061.10010    891.52269   8164.08900    515.37738     84.19589  -2307.55000 
##           32           33           34           35           36           37 
##   1631.38167   4650.17108   1266.11455   2536.28306  -1700.56099   4735.60181 
##           38           39           40           41           42           43 
##   4379.05946  -2148.83506  -2901.71094  -1081.48838 -10731.28991   7148.79297 
##           44           45           46           47           48           49 
##   2536.83936   1385.33292   8141.02671    831.33806   6665.84741   6926.02864 
##           50           51           52           53           54           55 
##  -5602.95959  -4632.83079  -4983.97795  -7932.41217   6016.33147  -4089.55021 
##           56           57           58           59           60           61 
##  -4961.97022   3728.92930    831.39936    -68.44308    110.62198  -5021.47030 
##           62           63           64           65           66           67 
##  18035.71110   3814.87187  -3442.38164   6053.13442   7539.15688  14912.70353 
##           68           69           70           71           72           73 
##   2137.73390 -12799.83007  -1129.05122   4780.77956  -4714.76118  -4310.08415 
##           74           75           76           77           78           79 
## -10475.62561   2340.33249  -5475.05141    923.48814  -6973.22134    358.52025 
##           80           81           82           83           84           85 
##  -2510.80362  -2862.43006  -4115.94091   -752.37239   2120.13599   3625.67631 
##           86           87           88           89           90           91 
##    410.21894   -535.59090    145.84073   4260.89934  -1138.35111   1156.79953 
##           92           93           94           95           96           97 
##  -2042.63201  -1053.35547    155.73482    258.78004  -7493.58145   2279.90109 
##           98           99          100          101          102          103 
##  -8666.24285  -3115.20178  -4232.09085  -1960.33151  -1479.96898   2973.24566 
##          104          105          106          107          108          109 
##  -2478.54783   2442.88817  -1253.69485    871.76221   2515.01666  -3180.73284 
##          110          111          112          113          114          115 
##  -4789.28586   -973.13128   1785.04255  11617.18188  -1146.01792   2736.28296 
##          116          117          118          119          120          121 
##   4359.84580   3647.44837   -923.99424  -4577.16674  -3667.37431   2318.26488 
##          122          123          124          125          126          127 
##  -1700.96500   1344.73515   8881.38314    991.27662    268.93270  -2397.72543 
##          128          129          130          131          132          133 
##   2728.65789   7154.47953   1200.36541  -8320.40334   1788.04740   4194.47418 
##          134          135          136          137          138          139 
##  -3054.21974  -1367.18633   -827.13013  -3867.73966   1140.48601   -515.51740 
##          140          141          142          143          144          145 
##  -2937.32284   1657.59297  -1909.43266  -7879.57301   1887.30115  -3583.64662 
##          146          147          148          149          150          151 
##   1963.64358   -348.72789    940.30701   -416.76050   1297.58472   1158.31647 
##          152          153          154          155          156          157 
##   3348.93624  -4821.17989  -1205.98200  -3279.04967   5874.56433   9758.31686 
##          158          159          160          161          162          163 
##  -3591.71260  -4964.51770   3376.14436     46.89242   2570.66258  -5982.04928 
##          164          165          166          167          168          169 
##  -6890.96048   3938.81168  17260.08091   3745.41758   -247.50500  -2320.14551 
##          170          171          172          173          174          175 
##  -1027.96969   3641.43655   -135.96864  -7998.77422   2820.57046   4325.56012 
##          176          177          178          179          180          181 
##    682.96268   8807.90998  -9081.59153  -3451.87813 -10775.26292 -11412.98331 
##          182          183          184          185          186          187 
##    931.20034   9043.58554  -1524.40204   5825.35115   6542.22517  13230.77368 
##          188          189          190          191          192          193 
##   8664.05528  -3753.94154   2674.06970  10577.23631  -1331.65239  -2200.94059 
##          194          195          196          197          198          199 
## -10106.72480  -6352.84597   1153.96648  -5290.88429  -9919.13968   5144.00128 
##          200          201          202          203          204          205 
##  -3209.28386  -1878.86730   -974.93177   6331.50127   9819.58434    645.56191 
##          206          207          208          209          210          211 
##   2984.58499   3182.21684   5892.20879  12996.96874  -5384.31993 -11111.39810 
##          212          213          214          215          216          217 
##  -5656.83168 -10656.77958  -5276.68263   1282.24083 -13206.80529  16046.73176 
##          218          219          220          221          222          223 
##   7719.30230   1545.58436  26717.94943  12888.73710   7804.24627  14522.38947 
##          224          225          226          227          228          229 
##  -3308.79238  -1271.55244   4157.46182    734.12476   3071.83328   9319.77886 
##          230          231          232          233          234          235 
##   6218.66356  -1494.10030  -1496.56378   9687.38252 -11162.29163  -7135.20023 
##          236          237          238          239          240          241 
##  -8510.70409 -10189.83563   2865.79403   1203.82320  -8412.34095  -9200.74540 
##          242          243          244          245          246          247 
##   8791.27256  -7915.87017   2252.42318 -10477.35340  -4343.81790   1114.33742 
##          248          249          250          251          252          253 
##    748.87211 -12528.50675   3295.82971   1802.34193   4007.02885   2005.32977 
##          254          255          256          257          258          259 
##  -1251.26777  11039.92133  20929.74086   3494.86716  -3980.50985   4300.34280 
##          260          261          262          263          264          265 
##  -1484.42376   3891.02347  -4678.18176 -10811.20563  -4801.38301   -651.24525 
##          266          267          268          269          270          271 
##  -5314.08810   8596.49370  -4336.03633   4079.36875  -2158.41906   4351.50961 
##          272          273          274          275          276          277 
##    686.70634   7283.61127  -1347.71819  12053.85122  -4422.04638   1798.44456 
##          278          279          280          281          282          283 
##   -298.15536   7900.91900  -4928.87275  -2690.97425 -11268.36202  -2819.57673 
##          284          285          286          287          288          289 
##  18484.71492   7852.77724   2878.00221   -481.18464   1016.06674   6494.40893 
##          290          291          292          293          294          295 
##   7032.24322 -18570.47364 -11195.04209  -8307.80356   9402.51797   2953.76288 
##          296          297          298          299          300          301 
##  -1251.58465  27318.73509  10307.16138   5219.14021   9841.93651   3239.56019 
##          302          303          304          305          306          307 
##   -676.21822   8184.28278 -23962.26282  -3534.10597   -220.33664  -7013.69597 
##          308          309          310          311          312          313 
##  -4095.51586   2776.20709  -9296.44174  -3428.09941  -8395.77066   1289.83849 
##          314          315          316          317          318          319 
##  -3372.60523   1819.23707  -4259.53803  27242.90869   -611.05828   3372.55369 
##          320          321          322          323          324          325 
##  10930.09597   5790.02124  32609.77161   5669.80437 -20401.25204   2014.11633 
##          326          327          328          329          330          331 
##   1317.02296  -6279.15310  -1651.12452 -33219.53514    583.04220  -2543.66771 
##          332          333          334          335          336          337 
##   -320.60230  -3358.80305   3891.76169   -553.71851  -7052.77783  -3274.00157 
##          338          339          340          341          342          343 
##  -2355.71235  -7838.96822   3636.11395  -1509.58633  -1866.75627  -1118.59024 
##          344          345          346          347          348          349 
##     63.73448    391.45956  -1686.31665  -9518.43287 -13370.87662   2032.03910 
##          350          351          352          353          354          355 
##  -4532.88726  -3880.99574  -6206.03181   1496.38970   1188.39584   2602.53699 
##          356          357          358          359          360          361 
##  -3864.41188   -639.01719    568.40333   6929.10028    279.78008    -31.34086 
##          362          363          364          365          366          367 
##   2589.86105  -2715.64782   -874.27694  -8746.93659  -4716.13142  -6333.16432 
##          368          369          370          371          372          373 
##  -5112.76248  -7439.03533   4784.39473    228.43277   7004.23368  -7659.01772 
##          374          375          376          377          378          379 
##  -2357.75534  -3487.99467  -2583.02291 -12577.38887   1675.88939 -10804.48296 
##          380          381          382          383          384          385 
##   5444.36928   9182.37316   3092.39668  -2393.88823   1585.75111   6745.76719 
##          386          387          388          389          390          391 
##  11481.41122  -5627.22366  -5277.20039   -142.43987   8572.47538   1907.36754 
##          392          393          394          395          396          397 
##  11315.74040  -9688.89480   2828.90988    782.93663    625.09688   -598.97223 
##          398          399          400          401          402          403 
##   -527.22952 -14466.43307   8396.63759  -1199.16495  -1398.63058   6946.99101 
##          404          405          406          407          408          409 
##  -7891.44556  -1338.99016  -2576.47478  -5877.86032  -2961.19130  -4026.59207 
##          410          411          412          413          414          415 
##  -8881.55846   5944.62467   1544.78029  -7434.86026  -7813.23182  14046.53628 
##          416          417          418          419          420          421 
##   3808.53731   4521.92387  -7967.13920  -4762.88139  -2659.30730   2749.75864 
##          422          423          424          425          426          427 
## -14038.28653  -2948.19764  -9254.05947   2796.66674   6827.37622   6519.77342 
##          428          429          430          431          432          433 
##  -3967.63712  -4139.29158  -4773.73736  -1874.91506  -5796.99165  -6750.49573 
##          434          435          436          437          438          439 
##  -6115.58435  -1588.50982  -1022.85908  -5127.66049   2407.29441   4721.07786 
##          440          441          442          443          444          445 
##  -5105.45702  -2245.07023   1486.05937  -3893.44563   2754.63757  -6613.90095 
##          446          447          448          449          450          451 
## -12201.12194  -4704.19778   9441.48227  -2102.79983   4680.12105  -5878.60764 
##          452          453          454          455          456          457 
##  -1182.54368    328.39641   2989.74404 -12262.30780   3256.87411  -6756.18737 
##          458          459          460          461          462          463 
##   6414.15901   2992.90305   2527.78115  -3795.89695   2103.25561     29.70314 
##          464          465          466          467          468          469 
##   1831.31361   -463.61465   3400.94650  -2555.46333   5857.15134  -6831.23962 
##          470          471          472          473          474          475 
##  -2931.30754  -2198.07244  -4670.70325   2952.29067   7798.75206  -5928.17117 
##          476          477          478          479          480          481 
##   1507.80140  -6134.58081  -2860.42093   1978.43709 -12928.88920  -9878.93238 
##          482          483          484          485          486          487 
##  -1400.90897   -153.93649  -1102.83381  -1463.85079  -9694.64643  10909.94385 
##          488          489          490          491          492          493 
##   6199.72302   7462.69542  -5315.33700   5423.55623   9404.89276   6253.57617 
##          494          495          496          497          498          499 
## -13232.09565 -10489.21816  -3475.04697  -1160.82957   -573.27559  -7663.66412 
##          500          501          502          503          504          505 
##    509.89580   4217.52761   5503.00169    722.44245    150.41248  -7168.42536 
##          506          507          508          509          510          511 
##    565.40101  -5036.10297   1800.75467  -1294.42972  -8159.47935   -678.43128 
##          512          513          514          515          516          517 
##  -2736.15520   -658.45652   1275.45072  -9519.96214  -7875.93305  24119.76423 
##          518          519          520          521          522          523 
##   9945.09016   6093.58402  -5081.93584   2969.11087  17203.24112  11816.43278 
##          524          525          526          527          528          529 
## -23728.12166  -4941.93866  -3676.32023   4590.75943   -285.02554 -11036.77760 
##          530          531          532          533          534          535 
##   4341.02231  13920.67548  -4814.66937   4468.51761   5689.04638  -1610.62949 
##          536          537          538          539          540          541 
##  -4397.94297  -6990.54479  -2093.57642   8315.83847    216.89343  -8054.56580 
##          542          543          544          545          546          547 
##   1814.83460   -572.51226    392.40073 -10995.45690 -11136.71410   1870.51733 
##          548          549          550          551          552          553 
##   6888.19637  -1334.10948    818.61129  -7718.06488   8496.44503    946.76642 
##          554          555          556          557          558          559 
## -11891.07453   9092.44715   8701.59625    234.57921   4978.29263  -3408.80830 
##          560          561          562          563          564          565 
##  14221.00606  21748.86207  -6022.71371  -9360.60230   6956.23577    458.89575 
##          566          567          568          569          570          571 
##   3663.63679  -7154.89460 -17185.41866   6598.71752   6459.55270   2000.67805 
##          572          573          574          575          576          577 
##   3212.38333   1909.99256  -2016.51367  14831.73621  -9388.59092  -6113.71051 
##          578          579          580          581          582          583 
##   8767.23275   3006.16076  -6380.04307   7591.73722  -3641.17304  -2669.71644 
##          584          585          586          587          588          589 
##  15774.11768 -14264.80404   8486.37183    217.88537  -6075.25754   -691.00580 
##          590          591          592          593          594          595 
##    308.97742 -10590.89153   1749.88522  -7154.67410   2995.32300   8847.73309 
##          596          597          598          599          600          601 
##  -7412.73049   5858.95608   2813.53290   6962.61925  -3015.83869   6276.54084 
##          602          603          604          605          606          607 
##  -8113.81645   2337.88791   1375.99026   3254.07705   1639.13770    548.35107 
##          608          609          610          611          612          613 
##  -5665.01962   8150.39089  -1021.57314  -2434.99714  -3348.28798  -8161.59257 
##          614          615          616          617          618          619 
##  11941.88591   5061.71402  -9141.64562  11688.92953   6240.28722  -5331.78445 
##          620          621          622          623          624          625 
##  26521.62732 -12389.05673  -6525.46983   3318.10512  -3972.79172 -10462.34819 
##          626          627          628          629          630          631 
##  11308.42183 -21489.67153  -2510.05697   8580.81158  11162.16115  -1394.97983 
##          632          633          634          635          636          637 
##  33417.25713  -6096.68834   6082.87765   5790.84797  -1857.83664  -5001.23583 
##          638          639          640          641          642          643 
##  -1687.25584 -12219.92640  -2181.95489  -1844.65531  -2491.00289  -2846.14360 
##          644          645          646          647          648          649 
##   1808.20656   4458.96392  17051.53108  18826.93567   1335.38740   5201.58549 
##          650          651          652          653          654          655 
##  11033.25078  20644.23769   1409.99766 -27463.50328  -1115.89785  -2094.01941 
##          656          657          658          659          660          661 
##   2031.07792  -3012.28685 -10483.81829   1682.80983   4278.50841   -896.40981 
##          662          663          664          665          666          667 
##  13133.88811   1613.08538   2064.89893 -11435.52265   1484.61362   1312.23204 
##          668          669          670          671          672          673 
##  -5026.13715  -7329.44397   2070.71085  -3665.39352   2689.63440  -3315.42303 
##          674          675          676          677          678          679 
##  -9302.04196  -8366.92456  -3111.41500     37.89898   2748.79663    678.45215 
##          680          681          682          683          684          685 
##  -3830.86735  -1839.62674  -1349.12956  -8267.29729   4547.16493  -2258.41192 
##          686          687          688          689          690 
##  -1421.15651    568.55454  10861.74250  10002.95486  10892.23080 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17330.18 20132.81 24320.25 24042.62 26359.17 23733.55 24438.67 19745.37 
##       10       11       12       13       14       15       16       17 
## 19486.05 16870.38 17635.69 14416.25 14466.61 15120.87 16790.90 15137.21 
##       18       19       20       21       22       23       24       25 
## 16155.69 15539.02 22510.66 21609.00 21097.00 22957.44 22293.97 22936.31 
##       26       27       28       29       30       31       32       33 
## 24753.39 18776.76 20475.91 28190.62 28247.38 27925.41 25591.90 26972.40 
##       34       35       36       37       38       39       40       41 
## 30755.31 31098.29 32485.42 30034.97 34063.94 37221.84 34324.00 31184.77 
##       42       43       44       45       46       47       48       49 
## 30050.58 20777.49 28178.59 30576.95 31649.12 38380.23 37882.72 42471.97 
##       50       51       52       53       54       55       56       57 
## 46641.96 39454.12 34107.55 29208.13 22459.81 28651.41 25285.54 21641.07 
##       58       59       60       61       62       63       64       65 
## 25980.46 27220.30 27512.66 27918.04 23853.57 40185.27 42000.38 37320.72 
##       66       67       68       69       70       71       72       73 
## 41461.84 46300.58 56801.84 54846.69 40320.77 37865.65 40836.33 35225.66 
##       74       75       76       77       78       79       80       81 
## 30749.05 21597.95 24749.34 20738.80 22792.22 17767.62 19751.52 18990.14 
##       82       83       84       85       86       87       88       89 
## 18033.08 16132.23 17390.01 20941.61 25290.21 26264.59 26289.16 26896.24 
##       90       91       92       93       94       95       96       97 
## 30956.78 29805.63 30789.35 28884.07 28096.41 28458.79 28859.01 22536.96 
##       98       99      100      101      102      103      104      105 
## 25504.81 18644.34 17518.38 15589.76 15884.83 16551.61 20954.26 20052.11 
##      106      107      108      109      110      111      112      113 
## 23508.27 23301.52 24951.41 27783.16 25320.43 21819.56 22090.67 24695.53 
##      114      115      116      117      118      119      120      121 
## 35390.02 33611.15 35419.87 38371.27 40296.57 38021.17 32923.23 29321.88 
##      122      123      124      125      126      127      128      129 
## 31372.11 29678.98 30842.05 38322.87 37970.92 37047.15 33959.77 35713.09 
##      130      131      132      133      134      135      136      137 
## 41026.49 40475.55 31814.95 33059.95 36199.79 32666.61 31079.13 30178.45 
##      138      139      140      141      142      143      144      145 
## 26789.37 28181.66 27954.89 25677.41 27670.15 26316.43 20018.70 23001.79 
##      146      147      148      149      150      151      152      153 
## 20862.50 23793.01 24324.55 25890.05 26069.27 27697.54 28977.92 31962.61 
##      154      155      156      157      158      159      160      161 
## 27503.70 26778.19 24371.72 30173.54 41612.14 39968.52 37374.71 42316.39 
##      162      163      164      165      166      167      168      169 
## 43702.91 47065.33 42602.25 37982.90 43323.20 59370.15 61547.65 59986.57 
##      170      171      172      173      174      175      176      177 
## 56861.97 55286.28 57946.54 56985.92 49398.72 52178.01 55862.04 55897.66 
##      178      179      180      181      182      183      184      185 
## 62914.88 53565.88 50367.69 41320.27 32992.09 36445.41 46390.69 45855.22 
##      186      187      188      189      190      191      192      193 
## 51714.77 57369.80 67983.94 73184.08 66977.50 67167.91 74127.51 69871.65 
##      194      195      196      197      198      199      200      201 
## 65464.58 54876.85 49000.46 50402.46 46066.14 38357.57 44681.71 42936.87 
##      202      203      204      205      206      207      208      209 
## 42580.50 43051.36 49738.99 58489.01 58124.42 59822.21 61452.08 65183.89 
##      210      211      212      213      214      215      216      217 
## 74502.18 66708.97 55082.97 49776.21 40913.54 37918.90 40983.81 31160.27 
##      218      219      220      221      222      223      224      225 
## 47867.98 55074.13 55961.91 78370.83 85748.47 87720.32 95192.79 86285.41 
##      226      227      228      229      230      231      232      233 
## 80377.82 79966.30 76668.74 75843.36 80506.19 81849.10 76371.71 71659.62 
##      234      235      236      237      238      239      240      241 
## 77224.72 64081.63 56242.85 48319.55 40062.49 44188.75 46307.77 39861.03 
##      242      243      244      245      246      247      248      249 
## 33639.58 43761.01 38098.01 41972.07 34357.10 33083.23 36681.27 39460.94 
##      250      251      252      253      254      255      256      257 
## 30434.03 36279.09 40020.97 45134.38 47810.12 47310.65 57450.26 74673.42 
##      258      259      260      261      262      263      264      265 
## 74491.37 67906.80 69365.42 65645.41 67068.90 60924.35 50366.95 46456.53 
##      266      267      268      269      270      271      272      273 
## 46662.66 42830.36 51496.61 47828.06 51909.85 50055.92 54059.58 54350.96 
##      274      275      276      277      278      279      280      281 
## 60274.15 57945.43 67466.90 61486.84 61693.58 60068.51 65721.44 59550.12 
##      282      283      284      285      286      287      288      289 
## 56167.79 45883.72 44305.57 61267.94 66711.43 67114.47 64572.50 63674.16 
##      290      291      292      293      294      295      296      297 
## 67612.47 71461.47 52755.61 43012.66 37117.48 47277.24 50468.30 49596.12 
##      298      299      300      301      302      303      304      305 
## 73413.55 79265.86 79923.06 84463.30 82690.08 77798.15 81210.69 56502.53 
##      306      307      308      309      310      311      312      313 
## 52822.19 52506.98 46394.37 43647.51 47194.44 39863.24 38605.34 33252.02 
##      314      315      316      317      318      319      320      321 
## 36977.32 36171.48 39942.97 37958.95 63341.63 61216.59 62814.76 70687.69 
##      322      323      324      325      326      327      328      329 
## 73037.66 98120.48 96523.54 72732.03 71548.69 69931.72 62009.41 59176.68 
##      330      331      332      333      334      335      336      337 
## 29595.39 33225.24 33657.89 35941.52 35292.67 40969.43 42028.21 37350.14 
##      338      339      340      341      342      343      344      345 
## 36576.86 36701.54 32093.74 37998.87 38651.90 38906.30 39768.41 41526.40 
##      346      347      348      349      350      351      352      353 
## 43319.89 43075.43 36130.45 26845.82 32106.89 30985.71 30582.17 28235.90 
##      354      355      356      357      358      359      360      361 
## 32841.60 36537.18 40930.98 39148.30 40388.88 42493.90 49773.51 50315.48 
##      362      363      364      365      366      367      368      369 
## 50514.00 52938.65 50461.42 49914.65 42674.85 39915.45 36152.19 33965.61 
##      370      371      372      373      374      375      376      377 
## 30085.03 37259.00 39510.19 47272.45 41338.33 40794.14 39354.31 38894.39 
##      378      379      380      381      382      383      384      385 
## 29904.82 34431.05 27591.35 35682.20 45853.75 49363.46 47663.82 49624.38 
##      386      387      388      389      390      391      392      393 
## 55747.30 65084.51 58401.91 52956.58 52689.52 59953.78 60468.97 69002.18 
##      394      395      396      397      398      399      400      401 
## 58278.09 59820.49 59387.47 58879.40 57389.94 56170.86 43136.36 51587.88 
##      402      403      404      405      406      407      408      409 
## 50603.92 49586.29 55887.59 48546.56 47868.47 46221.29 41966.05 40815.02 
##      410      411      412      413      414      415      416      417 
## 38909.13 33095.52 40845.36 43726.00 38481.52 33646.46 48285.89 52070.65 
##      418      419      420      421      422      423      424      425 
## 55938.57 48525.31 44906.02 43602.67 47133.14 35733.05 35466.49 29814.90 
##      426      427      428      429      430      431      432      433 
## 35317.48 43515.08 50299.64 47115.58 44230.02 41203.20 41093.13 37625.92 
##      434      435      436      437      438      439      440      441 
## 33824.58 31101.80 32653.29 34473.80 32509.56 37299.78 43408.46 40211.50 
##      442      443      444      445      446      447      448      449 
## 39922.08 42881.59 40800.65 44727.90 40048.98 31221.20 30076.80 41256.51 
##      450      451      452      453      454      455      456      457 
## 40943.02 46506.04 42210.26 42554.46 44149.68 47809.88 37842.13 42615.76 
##      458      459      460      461      462      463      464      465 
## 38110.41 45561.38 49026.50 51606.18 48386.74 50691.01 50889.40 52609.19 
##      466      467      468      469      470      471      472      473 
## 52114.62 55012.46 52382.42 57354.81 50719.88 48368.07 46976.27 43653.28 
##      474      475      476      477      478      479      480      481 
## 47350.82 54697.74 49211.63 50888.30 45758.42 44162.71 46951.46 36530.79 
##      482      483      484      485      486      487      488      489 
## 30192.77 32032.94 34687.55 36154.28 37105.08 30845.06 43179.85 49736.16 
##      490      491      492      493      494      495      496      497 
## 56459.91 51253.87 56011.54 63526.14 67278.10 53748.79 44473.62 42529.40 
##      498      499      500      501      502      503      504      505 
## 42847.56 43626.38 38199.10 40560.62 45779.43 51372.41 52071.02 52179.85 
##      506      507      508      509      510      511      512      513 
## 45980.03 47299.10 43616.67 46329.14 46000.05 39813.86 40927.30 40115.31 
##      514      515      516      517      518      519      520      521 
## 41203.69 43802.53 36754.36 32107.38 55624.34 63657.70 67253.65 60736.03 
##      522      523      524      525      526      527      528      529 
## 62054.62 75428.28 82296.12 57637.22 52587.32 49333.24 53643.88 53157.92 
##      530      531      532      533      534      535      536      537 
## 43494.69 48408.61 60871.53 55477.91 58822.53 62748.06 59846.66 54954.97 
##      538      539      540      541      542      543      544      545 
## 48519.29 47196.16 55009.39 54763.71 47439.88 49628.80 49458.17 50141.17 
##      546      547      548      549      550      551      552      553 
## 40936.14 32899.34 37173.38 45163.25 44963.39 46642.64 40745.98 49618.23 
##      554      555      556      557      558      559      560      561 
## 50755.50 40694.27 50086.26 57826.28 57201.14 60742.67 56575.99 68152.85 
##      562      563      564      565      566      567      568      569 
## 84580.86 74826.60 63568.76 67918.96 66072.65 67240.75 58942.42 43181.57 
##      570      571      572      573      574      575      576      577 
## 50080.73 55893.61 57057.90 59101.01 59737.94 56909.26 68964.59 58504.00 
##      578      579      580      581      582      583      584      585 
## 52325.05 59807.84 61288.33 54490.26 60658.89 56304.15 53394.88 66752.95 
##      586      587      588      589      590      591      592      593 
## 52409.20 59638.69 58745.26 52565.58 51881.59 52153.32 43014.26 45767.39 
##      594      595      596      597      598      599      600      601 
## 40477.82 44657.27 53283.59 46719.04 52486.47 54827.10 60407.55 56625.74 
##      602      603      604      605      606      607      608      609 
## 61364.25 53064.68 54915.30 55679.49 57951.58 58516.65 58064.59 52333.04 
##      610      611      612      613      614      615      616      617 
## 59284.29 57374.71 54517.29 51274.88 44347.83 55678.14 59504.79 50581.93 
##      618      619      620      621      622      623      624      625 
## 60821.28 64940.78 58532.37 80412.34 65767.76 58217.04 60188.65 55614.63 
##      626      627      628      629      630      631      632      633 
## 46101.15 56641.10 37501.49 37363.90 46782.55 57101.27 55176.46 83456.12 
##      634      635      636      637      638      639      640      641 
## 73795.84 75962.15 77573.84 72382.66 65215.83 61902.78 49996.95 48390.80 
##      642      643      644      645      646      647      648      649 
## 47299.72 45805.72 44215.65 46850.61 51395.75 66132.35 80330.90 77499.27 
##      650      651      652      653      654      655      656      657 
## 78388.89 84168.48 97402.72 92243.36 62978.75 60470.45 57472.49 58441.72 
##      658      659      660      661      662      663      664      665 
## 54938.39 45501.19 47848.21 52098.41 51303.25 62684.06 62563.67 62848.67 
##      666      667      668      669      670      671      672      673 
## 51484.81 52823.05 53825.57 49237.30 43311.29 46298.68 43935.08 47367.28 
##      674      675      676      677      678      679      680      681 
## 45154.90 38104.64 32846.27 32843.82 35549.77 40207.69 42432.72 40468.48 
##      682      683      684      685      686      687      688      689 
## 40491.70 40933.44 35364.41 41594.70 41100.01 41394.59 43358.83 53898.90 
##      690 
## 62223.77 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8246
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    8.075634  0.5060371    3.504197
## t2* 1914.405267 23.6052133  226.306327
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    3.491382       8.211244   14.8626
## 2    lag_depvar 1585.695012    1926.351453 2329.9042

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Apr 01 00:42:04 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 01 00:42:11 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 01 00:42:18 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 01 00:42:25 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 01 00:42:32 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 01 00:42:39 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 01 00:42:46 2024
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## =-=-=-=-= Iteration 14000 Mon Apr 01 00:42:53 2024
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## =-=-=-=-= Iteration 16000 Mon Apr 01 00:43:00 2024
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## =-=-=-=-= Iteration 18000 Mon Apr 01 00:43:07 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 5.516333 5.195333 5.410333 5.849167 6.5345098
Comida 288.100000 366.009167 310.278417 317.896583 343.3277255
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.0000000
Electricidad 74.633333 38.104750 47.072333 29.523000 36.2265882
Enceres 38.339333 18.259750 20.086417 14.801167 23.9398039
Farmacia 0.000000 4.733250 1.831667 13.996083 8.1406471
Gas/Bencina 23.665667 35.219333 44.325000 13.583667 27.3653529
Diosi 29.350333 55.804250 31.180667 52.687833 43.3250980
donaciones/regalos 0.000000 0.000000 0.000000 14.340167 5.3866471
Electrodomésticos/ Mantención casa 20.000000 0.000000 3.944000 56.595000 17.4405490
VTR 21.990000 12.829167 25.156667 19.086917 19.2197647
Netflix 5.565667 4.555500 7.151583 7.028750 6.6763529
Otros 0.000000 0.000000 3.151083 0.000000 0.7414314
Total 507.160667 540.710500 499.588167 545.388333 538.3244706
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2291, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-04-09 00:04:58 sería de: 37.761 pesos// Percentil 95% más alto proyectado: 40.872,77

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 37183.29 37180.57
Lo.80 37214.14 37215.55
Point.Forecast 37760.78 39294.66
Hi.80 39520.43 44169.68
Hi.95 40484.89 46750.36


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2841  1016.1292
## s.e.  0.1275    29.2255
## 
## sigma^2 = 27810:  log likelihood = -397.69
## AIC=801.38   AICc=801.81   BIC=807.72
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2520   697.6354  10.1356
## s.e.  0.1296   268.0070   8.4706
## 
## sigma^2 = 27678:  log likelihood = -397.02
## AIC=802.03   AICc=802.75   BIC=810.47
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 771.1511 675.2312 745.5439
Lo.80 887.7787 793.2280 832.9201
Point.Forecast 1108.0936 1016.1291 1026.8549
Hi.80 1328.4084 1239.0303 1265.8909
Hi.95 1445.0360 1357.0271 1414.1699


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))